microsoft--promptflow/examples/flows/evaluation/eval-groundedness

Groundedness Evaluation

This is a flow leverage llm to eval groundedness: whether answer is stating facts that are all present in the given context.

Tools used in this flow：

• python tool
• built-in llm tool

0. Setup connection

Prepare your Azure OpenAI resource follow this instruction and get your api_key if you don't have one.

# Override keys with --set to avoid yaml file changes
pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base>

1. Test flow/node

# test with default input value in flow.dag.yaml
pf flow test --flow .

2. create flow run with multi line data

pf run create --flow . --data ./data.jsonl --column-mapping question='${data.question}' answer='${data.answer}' context='${data.context}' --stream

You can also skip providing column-mapping if provided data has same column name as the flow. Reference here for default behavior when column-mapping not provided in CLI.